Adaptive filters have been widely applied for many years. An adaptive filter comprises a linear filter system with a transfer function between an input signal and an output signal, the transfer function comprising coefficients which can be controlled to optimise some measure of the output signal, for instance to minimise the error between the output signal and a supplied reference signal. An adaptive filter also comprises some adaptation control mechanism to control the coefficients. The coefficients may be initially set to some initial values, and are then controlled to converge over time to the optimum value based on the input signal and reference signal present. As with control loops in general, the adaptation of the coefficients may occur more quickly or more slowly or be over-damped or under-damped based on parameters of the design of the adaptation control mechanism, i.e. based on adaptation parameters or convergence factors of the adaptive filter.
In applications such as speech enhancement and acoustic noise cancellation, adaptive filters can be used to estimate the acoustic echo path for echo cancellation. In the case of a device with multiple microphones operating in a hands-free mode, adaptive filters can be used to model the speech path or interference paths in order to adaptively remove noise from a desired speech signal.
In multi-microphone applications, especially in devices with a small number of closely spaced microphones, each microphone may pick up significant amounts of both the desired speech signal and undesired background noise. The speech and noise components may be separated by using two or more adaptive filters. However it is preferable to adapt some filters when speech is present and to adapt others when only the background noise is present. This adaption mode control may be driven by a signal to noise ratio (SNR) measurement, using a threshold value to determine when speech is present and adapting one or more filters depending on the result of this determination. However, it is difficult to produce an accurate measurement of the signal-to-noise ratio and to thence derive reliable decisions, especially in devices with a small number of microphones or with particularly non-stationary noise conditions.
Another disadvantage of using SNR based mode control is that it assumes that the SNR of a designated voice microphone is always higher than that of a designated noise microphone. This could be true when the device is in use as a handset, when the voice microphone is very close to the user's mouth. However, this is not always true in practice, for example when the device is in use as a speakerphone. For example, the handheld handset could be rotated, or the user could walk around a table on which the handset is positioned with an arbitrary orientation. Or it could be that the voice microphone is physically further away from the user's mouth than the noise microphone, in order to be well separated from the loudspeaker for better echo performance. In these situations, the SNR measured in the voice microphone could be similar to, or even lower than, that of the noise microphone and the false decision made from SNR measurement could finally result in heavy speech distortion.
Other methods involve different methods of speech detection, but these are also difficult to use in the limited conditions imposed by handheld devices.